class_docstr: Transformer-TTS module.
  
  This is a module of text-to-speech Transformer described in `Neural Speech Synthesis
  with Transformer Network`_, which convert the sequence of tokens into the sequence
  of Mel-filterbanks.
  
  .. _`Neural Speech Synthesis with Transformer Network`:
      https://arxiv.org/pdf/1809.08895.pdf
docstr: Make masks for masked self-attention.
  
  Args:
      olens (LongTensor): Batch of lengths (B,).
  
  Returns:
      Tensor: Mask tensor for masked self-attention.
          dtype=torch.uint8 in PyTorch 1.2-
          dtype=torch.bool in PyTorch 1.2+ (including 1.2)
  
  Examples:
      >>> olens = [5, 3]
      >>> self._target_mask(olens)
      tensor([[[1, 0, 0, 0, 0],
               [1, 1, 0, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 1, 0],
               [1, 1, 1, 1, 1]],
              [[1, 0, 0, 0, 0],
               [1, 1, 0, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 0, 0],
               [1, 1, 1, 0, 0]]], dtype=torch.uint8)
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code: def _target_mask(self, olens: torch.Tensor) -> torch.Tensor:
      """Make masks for masked self-attention.
  
      Args:
          olens (LongTensor): Batch of lengths (B,).
  
      Returns:
          Tensor: Mask tensor for masked self-attention.
              dtype=torch.uint8 in PyTorch 1.2-
              dtype=torch.bool in PyTorch 1.2+ (including 1.2)
  
      Examples:
          >>> olens = [5, 3]
          >>> self._target_mask(olens)
          tensor([[[1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 1, 0],
                   [1, 1, 1, 1, 1]],
                  [[1, 0, 0, 0, 0],
                   [1, 1, 0, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 0, 0],
                   [1, 1, 1, 0, 0]]], dtype=torch.uint8)
  
      """
      y_masks = make_non_pad_mask(olens).to(
          next(self.parameters()).device
      )
      s_masks = subsequent_mask(
          y_masks.size(-1), device=y_masks.device
      ).unsqueeze(0)
      return y_masks.unsqueeze(-2) & s_masks
  
imports: from typing import Dict, Optional, Sequence, Tuple
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.torch_utils.initialize import initialize
  from espnet2.tts.abs_tts import AbsTTS
  from espnet2.tts.gst.style_encoder import StyleEncoder
  from espnet.nets.pytorch_backend.e2e_tts_transformer import GuidedMultiHeadAttentionLoss, TransformerLoss
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, make_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.decoder import Prenet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.decoder import Decoder
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask
label_desc: Use image node_id 8 for calling the Transformer obj's underlying member method code with example usage: obj._target_mask(olens) and returns: unknown